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内网环境使用Docker部署Qwen2模型_qwen2的docker镜像
作者：weixin_40725706 | 2024-07-04 18:46:30
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qwen2的docker镜像
背景介绍
在我参与的一个国企项目中，我们基于大语言模型开发了一些应用，但是甲方公司所有的资源环境都是纯内网。更为有趣的是，甲方公司已自主搭建并运行着一套百度机器学习平台（BML），客户要求所有的大模型部署必须依托于现有的BML平台进行，而非独立构建全新的基础设施，资源申请也相当严苛。面对这一系列限定条件，我们只能试着利用Docker容器技术进行大语言模型的部署。
前期准备
1、首先，内网环境部署docker：
这部分内容不再赘述，可参考之前写的教程。
https://zyn1994.blog.csdn.net/article/details/109516191
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2、其次，使用一台具备网络环境的设备，拉取ollama的基础镜像：
docker pull ollama/ollama:latest
# 如果拉取不到，可使用下面这个
docker pull dhub.kubesre.xyz/ollama/ollama:latest
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3、下载Qwen2的GGUF模型，这里为了演示方便就下载0.5B的模型了。
下载地址：https://huggingface.co/Qwen/Qwen2-0.5B-Instruct-GGUF或者https://modelscope.cn/models/qwen/Qwen2-0.5B-Instruct-GGUF
4、编写Modelfile文件：
# 注意GGUF模型文件的地址要与Dockerfile中保持一致
FROM /tmp/qwen2-0_5b-instruct-q4_0.gguf
TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>
"
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>
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最终得到GGUF模型文件和Modelfile文件。
-rw-r--r--. 1 root root 290 Jun 21 14:00 Modelfile
-rw-r--r--. 1 root root 352969408 Jun 21 13:44 qwen2-0_5b-instruct-q4_0.gguf
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构建镜像
1、将先前拉取的基础镜像导入内网设备，然后编写Dockerfile文件：
FROM ollama:latest
EXPOSE 11434
ADD Modelfile /tmp/Modelfile
ADD qwen2-0_5b-instruct-q4_0.gguf /tmp/qwen2-0_5b-instruct-q4_0.gguf
ENTRYPOINT ["sh","-c","/bin/ollama serve"]
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2、构建docker镜像，执行docker build -t ollama_qwen2-0_5b:1.0 -f Dockerfile .：
(base) [root@localhost docker-qwen2]# docker build -t ollama_qwen2-0_5b:1.0 -f Dockerfile . [+] Building 1.7s (8/8) FINISHED docker:default => [internal] load .dockerignore 0.4s => => transferring context: 2B 0.0s => [internal] load build definition from Dockerfile 0.5s => => transferring dockerfile: 303B 0.0s => [internal] load metadata for docker.io/library/ollama:latest 0.0s => [1/3] FROM docker.io/library/ollama:latest 0.0s => [internal] load build context 0.1s => => transferring context: 201B 0.0s => CACHED [2/3] ADD Modelfile /tmp/Modelfile 0.0s => CACHED [3/3] ADD qwen2-0_5b-instruct-q4_0.gguf /tmp/qwen2-0_5b-instruct-q4_0.gguf 0.0s => exporting to image 0.1s => => exporting layers 0.0s => => writing image sha256:a6a949928f9bffffe1fbc5ee2c1002bd76afd9a9579dc10c6598faebb57a4885 0.0s => => naming to docker.io/library/ollama_qwen2-0_5b:1.0
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运行容器
1、创建并运行容器，执行docker run -itd --name ollama_qwen2 -p 11434:11434 ollama_qwen2-0_5b:1.0
(base) [root@localhost docker-qwen2]# docker run -itd --name ollama_qwen2 -p 11434:11434 ollama_qwen2-0_5b:1.0
b034390bf79ceca1ec67bb4f9898c930c2a6efe8260bb8ba0fcbe5ffd2634f1a
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2、验证docker容器是否执行成功：
(base) [root@localhost docker-qwen2]# docker ps -a
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0341f573e41f ollama_qwen2-0_5b:1.0 "sh -c '/bin/ollama …" 6 seconds ago Up 3 seconds 0.0.0.0:11434->11434/tcp, :::11434->11434/tcp ollama_qwen2
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到这里，我们已经部署好了docker版的ollama。这时ollama里并没有运行任何的模型，还需要我们进入容器创建加载一下。
加载Qwen2模型
1、首先进入我们刚刚运行的容器：
docker exec -it 0341f573e41f /bin/bash
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2、执行ollama create命令，创建及加载Qwen2模型：
root@0341f573e41f:/# ollama create qwen:0.5b -f /tmp/Modelfile
transferring model data
using existing layer sha256:aca679832ded61145239ce7f5c5ebddb1c57ada786c9c23733899c3888e0596f
creating new layer sha256:62fbfd9ed093d6e5ac83190c86eec5369317919f4b149598d2dbb38900e9faef
creating new layer sha256:f02dd72bb2423204352eabc5637b44d79d17f109fdb510a7c51455892aa2d216
creating new layer sha256:a82ce90cbc26a4c59e0985d2bceffa1d2616a1579218ff3cb656a3252cafdcc0
writing manifest
success
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3、以上内容显示Qwen2模型已经成功在ollama中运行，然后输入exit退出容器即可。
问答测试
1、基于我自己写的open-ai-java的框架，访问Ollama服务的代码：
# 访问代码
public static void test0(){
OpenAIChat openAIChat = OpenAIChat.builder()
.endpointUrl("http://10.8.xxx.xxx:11434/v1")
.model("qwen:0.5b")
.build().init();
String stringFlux = openAIChat.chat("0dbe1580-60ae-4440-9462-df0a8f629f2c","你好");
System.out.println(stringFlux);
}
# Idea中的响应日志
17:05:32.370 [main] INFO com.xxx.openai.llms.OpenAIChat - OpenAI 请求参数: {top_p=0.78, max_tokens=20000, temperature=0.9, messages=[Message(role=user, content=你好)], model=qwen:0.5b}
17:05:34.547 [main] INFO com.xxx.openai.llms.OpenAIChat - OpenAI 处理成功 响应结果为:
您好！有什么我可以帮助您的吗？
{"id":"chatcmpl-675","object":"chat.completion","created":1718960749,"model":"qwen:0.5b","system_fingerprint":"fp_ollama","choices":[{"index":0,"message":{"role":"assistant","content":"您好！有什么我可以帮助您的吗？"},"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":9,"total_tokens":18}}
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2、查询docker容器中的日志，可以看到服务运行良好：
(base) [root@localhost docker-qwen2]# docker logs -f 0341f573e41f Couldn't find '/root/.ollama/id_ed25519'. Generating new private key. Your new public key is: ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAID82w1ycq7Ej68YHdCJHVPh4xOy09uzzzCk2c9hvJcvg 2024/06/21 09:00:37 routes.go:1060: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-06-21T09:00:37.756Z level=INFO source=images.go:725 msg="total blobs: 0" time=2024-06-21T09:00:37.756Z level=INFO source=images.go:732 msg="total unused blobs removed: 0" time=2024-06-21T09:00:37.756Z level=INFO source=routes.go:1106 msg="Listening on [::]:11434 (version 0.1.45)" time=2024-06-21T09:00:37.757Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama1087015210/runners time=2024-06-21T09:00:40.123Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60101]" time=2024-06-21T09:00:40.125Z level=INFO source=types.go:98 msg="inference compute" id=0 library=cpu compute="" driver=0.0 name="" total="62.6 GiB" available="41.1 GiB" [GIN] 2024/06/21 - 09:02:27 | 404 | 2.961658ms | 10.8.10.196 | POST "/v1/chat/completions" [GIN] 2024/06/21 - 09:04:31 | 200 | 20.105µs | 127.0.0.1 | HEAD "/" [GIN] 2024/06/21 - 09:05:05 | 200 | 13.876µs | 127.0.0.1 | HEAD "/" [GIN] 2024/06/21 - 09:05:06 | 201 | 854.761714ms | 127.0.0.1 | POST "/api/blobs/sha256:aca679832ded61145239ce7f5c5ebddb1c57ada786c9c23733899c3888e0596f" [GIN] 2024/06/21 - 09:05:08 | 200 | 1.59888621s | 127.0.0.1 | POST "/api/create" time=2024-06-21T09:05:48.013Z level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=25 layers.offload=0 layers.split="" memory.available="[41.1 GiB]" memory.required.full="662.7 MiB" memory.required.partial="0 B" memory.required.kv="24.0 MiB" memory.required.allocations="[662.7 MiB]" memory.weights.total="217.0 MiB" memory.weights.repeating="79.1 MiB" memory.weights.nonrepeating="137.9 MiB" memory.graph.full="298.5 MiB" memory.graph.partial="405.0 MiB" time=2024-06-21T09:05:48.013Z level=INFO source=server.go:359 msg="starting llama server" cmd="/tmp/ollama1087015210/runners/cpu_avx2/ollama_llama_server --model /root/.ollama/models/blobs/sha256-aca679832ded61145239ce7f5c5ebddb1c57ada786c9c23733899c3888e0596f --ctx-size 2048 --batch-size 512 --embedding --log-disable --parallel 1 --port 36844" time=2024-06-21T09:05:48.047Z level=INFO source=sched.go:382 msg="loaded runners" count=1 time=2024-06-21T09:05:48.047Z level=INFO source=server.go:547 msg="waiting for llama runner to start responding" time=2024-06-21T09:05:48.048Z level=INFO source=server.go:585 msg="waiting for server to become available" status="llm server error" INFO [main] build info | build=1 commit="7c26775" tid="139630217590656" timestamp=1718960748 INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="139630217590656" timestamp=1718960748 total_threads=8 INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="36844" tid="139630217590656" timestamp=1718960748 llama_model_loader: loaded meta data with 26 key-value pairs and 290 tensors from /root/.ollama/models/blobs/sha256-aca679832ded61145239ce7f5c5ebddb1c57ada786c9c23733899c3888e0596f (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = qwen2 llama_model_loader: - kv 1: general.name str = qwen2-0_5b-instruct llama_model_loader: - kv 2: qwen2.block_count u32 = 24 llama_model_loader: - kv 3: qwen2.context_length u32 = 32768 llama_model_loader: - kv 4: qwen2.embedding_length u32 = 896 llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 4864 llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 14 llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 2 llama_model_loader: - kv 8: qwen2.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 9: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 10: general.file_type u32 = 2 llama_model_loader: - kv 11: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 12: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... time=2024-06-21T09:05:48.299Z level=INFO source=server.go:585 msg="waiting for server to become available" status="llm server loading model" llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 19: tokenizer.chat_template str = {% for message in messages %}{% if lo... llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - kv 22: quantize.imatrix.file str = ../Qwen2/gguf/qwen2-0_5b-imatrix/imat... llama_model_loader: - kv 23: quantize.imatrix.dataset str = ../sft_2406.txt llama_model_loader: - kv 24: quantize.imatrix.entries_count i32 = 168 llama_model_loader: - kv 25: quantize.imatrix.chunks_count i32 = 1937 llama_model_loader: - type f32: 121 tensors llama_model_loader: - type q4_0: 165 tensors llama_model_loader: - type q4_1: 3 tensors llama_model_loader: - type q8_0: 1 tensors llm_load_vocab: special tokens cache size = 293 llm_load_vocab: token to piece cache size = 0.9338 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = qwen2 llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 151936 llm_load_print_meta: n_merges = 151387 llm_load_print_meta: n_ctx_train = 32768 llm_load_print_meta: n_embd = 896 llm_load_print_meta: n_head = 14 llm_load_print_meta: n_head_kv = 2 llm_load_print_meta: n_layer = 24 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 7 llm_load_print_meta: n_embd_k_gqa = 128 llm_load_print_meta: n_embd_v_gqa = 128 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 4864 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 32768 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 1B llm_load_print_meta: model ftype = Q4_0 llm_load_print_meta: model params = 494.03 M llm_load_print_meta: model size = 330.95 MiB (5.62 BPW) llm_load_print_meta: general.name = qwen2-0_5b-instruct llm_load_print_meta: BOS token = 151643 '<|endoftext|>' llm_load_print_meta: EOS token = 151645 '<|im_end|>' llm_load_print_meta: PAD token = 151643 '<|endoftext|>' llm_load_print_meta: LF token = 148848 'ÄĬ' llm_load_print_meta: EOT token = 151645 '<|im_end|>' llm_load_tensors: ggml ctx size = 0.14 MiB llm_load_tensors: CPU buffer size = 330.95 MiB llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 24.00 MiB llama_new_context_with_model: KV self size = 24.00 MiB, K (f16): 12.00 MiB, V (f16): 12.00 MiB llama_new_context_with_model: CPU output buffer size = 0.58 MiB llama_new_context_with_model: CPU compute buffer size = 298.50 MiB llama_new_context_with_model: graph nodes = 846 llama_new_context_with_model: graph splits = 1 INFO [main] model loaded | tid="139630217590656" timestamp=1718960748 time=2024-06-21T09:05:48.801Z level=INFO source=server.go:590 msg="llama runner started in 0.75 seconds" [GIN] 2024/06/21 - 09:05:49 | 200 | 1.930408217s | 10.8.10.196 | POST "/v1/chat/completions"
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